| /external/tensorflow/tensorflow/compiler/xla/tests/ |
| outfeed_in_nested_computation_test.cc | 32 Shape int_shape = ShapeUtil::MakeShape(xla::S32, {}); local
34 ShapeUtil::MakeTupleShape({int_shape, state_tuple_array_shape});
38 XlaOp num_iter = Infeed(&b, int_shape);
46 Outfeed(loop_counter, int_shape, "");
90 local_client_->TransferFromOutfeed(&int_shape));
111 local_client_->TransferFromOutfeed(&int_shape));
|
| /external/tensorflow/tensorflow/python/keras/layers/ |
| wrappers.py | 158 def _get_shape_tuple(self, init_tuple, tensor, start_idx, int_shape=None):
170 int_shape: an alternative static shape to take as the last part
174 The new int_shape with the first part from init_tuple
175 and the last part from either `int_shape` (if provided)
179 # replace all None in int_shape by K.shape
180 if int_shape is None:
181 int_shape = K.int_shape(tensor)[start_idx:]
182 if not any(not s for s in int_shape):
183 return init_tuple + tuple(int_shape)
[all...] |
| lstm_test.py | 221 np.zeros(keras.backend.int_shape(layer.states[0])),
223 state_shapes = [keras.backend.int_shape(state) for state in layer.states]
230 np.ones(keras.backend.int_shape(layer.states[0])),
|
| recurrent_v2.py | 218 input_shape = K.int_shape(inputs)
353 input_shape = K.int_shape(inputs)
589 input_shape = K.int_shape(inputs)
753 input_shape = K.int_shape(inputs)
[all...] |
| convolutional_recurrent.py | 312 shape = K.int_shape(state)
319 self.constants_spec = [InputSpec(shape=K.int_shape(constant))
368 timesteps = K.int_shape(inputs)[1]
[all...] |
| lstm_v2_test.py | 196 np.zeros(keras.backend.int_shape(layer.states[0])),
198 state_shapes = [keras.backend.int_shape(state) for state in layer.states]
205 np.ones(keras.backend.int_shape(layer.states[0])),
[all...] |
| recurrent.py | 650 InputSpec(shape=K.int_shape(state)) for state in initial_state
656 InputSpec(shape=K.int_shape(constant)) for constant in constants
708 input_shape = K.int_shape(nest.flatten(inputs)[0])
710 input_shape = K.int_shape(inputs)
[all...] |
| /external/tensorflow/tensorflow/python/keras/ |
| optimizers.py | 195 shapes = [K.int_shape(p) for p in params]
257 accumulators = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
324 shapes = [K.int_shape(p) for p in params]
397 shapes = [K.int_shape(p) for p in params]
494 ms = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
495 vs = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
497 vhats = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
583 shapes = [K.int_shape(p) for p in params]
673 shapes = [K.int_shape(p) for p in params]
|
| callbacks_v1.py | 187 shape = K.int_shape(w_img)
191 shape = K.int_shape(w_img)
198 shape = K.int_shape(w_img)
207 shape = K.int_shape(w_img)
|
| callbacks.py | [all...] |
| metrics.py | [all...] |
| backend.py | 720 v._keras_shape = int_shape(value)
932 # To get integer shape (Instead, you can use K.int_shape(x))
942 @keras_export('keras.backend.int_shape')
943 def int_shape(x): function
956 >>> K.int_shape(input)
960 >>> K.int_shape(kvar)
[all...] |
| backend_test.py | 169 self.assertEqual(keras.backend.int_shape(x), (3, 4))
173 self.assertEqual(keras.backend.int_shape(x), (None, 4))
[all...] |
| /external/tensorflow/tensorflow/python/keras/saving/ |
| hdf5_format.py | 458 if K.int_shape(layer.weights[0]) != weights[0].shape:
816 if K.int_shape(symbolic_weights[i]) != weight_values[i].shape:
819 ' has shape {}'.format(K.int_shape(
[all...] |
| /external/tensorflow/tensorflow/contrib/tpu/python/tpu/ |
| keras_tpu_variables.py | 337 v._keras_shape = backend.int_shape(value)
|
| /external/tensorflow/tensorflow/contrib/keras/api/keras/backend/ |
| __init__.py | 79 from tensorflow.python.keras.backend import int_shape
|
| /external/tensorflow/tensorflow/python/keras/engine/ |
| base_layer.py | [all...] |
| topology_test.py | [all...] |
| network.py | 314 self._feed_input_shapes.append(backend.int_shape(self.inputs[i]))
[all...] |
| training.py | 391 shape = K.int_shape(self.outputs[i])
[all...] |
